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Applications of convolutional neural networks to turbulence

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TURW04 - Wall-bounded turbulence: beyond current boundaries

Application of machine learning is currently one of the hottest topics in the fluid mechanics field. While machine learning seems to have a great possibility, its limitations should also be clarified. In our group, we have started a research project to construct a nonlinear feature extraction method by applying machine learning technology to “turbulence big data,” extracting the nonlinear modes essential to the regeneration mechanism of turbulence, and deriving the time evolution equation of those nonlinear modes. In this presentation, we will introduce some examples on learning and regeneration of temporal evolution of cross-sectional velocity field in a turbulent channel flow using convolutional neural network (CNN). We will also introduce the application of CNN for super-resolution analysis and reduced order modeling of turbulence. We also introduce our attempts to interpret the nonlinear modes extracted by CNN autoencoder and to use them for an advanced design of flow control, as well as an attempt for uncertainty quantification and applications to experimental data.

This talk is part of the Isaac Newton Institute Seminar Series series.

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